000 02896nam a22004575i 4500
001 978-1-4419-9326-7
003 DE-He213
005 20140220083234.0
007 cr nn 008mamaa
008 120216s2012 xxu| s |||| 0|eng d
020 _a9781441993267
_9978-1-4419-9326-7
024 7 _a10.1007/978-1-4419-9326-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aZhang, Cha.
_eeditor.
245 1 0 _aEnsemble Machine Learning
_h[electronic resource] :
_bMethods and Applications /
_cedited by Cha Zhang, Yunqian Ma.
264 1 _aBoston, MA :
_bSpringer US,
_c2012.
300 _aVIII, 329p. 84 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction of Ensemble Learning -- Boosting Algorithms: Theory, Methods and Applications -- On Boosting Nonparametric Learners -- Super Learning -- Random Forest -- Ensemble Learning by Negative Correlation Learning -- Ensemble Nystrom Method -- Object Detection -- Ensemble Learning for Activity Recognition -- Ensemble Learning in Medical Applications -- Random Forest for Bioinformatics.
520 _aIt is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
650 0 _aEngineering.
650 0 _aComputer science.
650 0 _aData mining.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputer Science, general.
700 1 _aMa, Yunqian.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441993250
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-9326-7
912 _aZDB-2-ENG
999 _c100571
_d100571